Papers by Kyungmin Kim
Fooling the LVLM Judges: Visual Biases in LVLM-Based Evaluation (2025.emnlp-main)
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| Challenge: | a recent study has found that large vision–language models are vulnerable to visual biases that inflate scores without altering semantic content. |
| Approach: | They propose a novel meta-evaluation benchmark that exhibits diverse score distributions. |
| Outcome: | The proposed model exhibits vulnerability across all domains, and combines multiple biases amplifies their effects, and pairwise evaluations are similarly susceptible. |
Mitigating Hallucinations in Large Vision-Language Models via Summary-Guided Decoding (2025.findings-naacl)
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| Challenge: | Large Vision-Language Models (LVLMs) generate detailed and coherent responses from visual inputs but are prone to generate hallucinations due to an over-reliance on language priors. |
| Approach: | They propose a method that reduces the text context and controls only the image-related POS tokens to maintain text quality by reducing the text contextualization. |
| Outcome: | The proposed method achieves state-of-the-art performance on object hallucination benchmarks and achieves Pareto optimality among the existing methods. |
Selective Perception: Learning Concise State Descriptions for Language Model Actors (2024.naacl-short)
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| Challenge: | Recent large language models support longer contexts, but requiring them to process redundant or irrelevant data increases inference time and cost. |
| Approach: | They propose a method that leverages a small finetuned LM to sample the minimal set of input features that maximizes the performance of a downstream LM. |
| Outcome: | The proposed method reduces the length of LM actor input by 87% and 99% while improving task success rates by 158% and 54% on NetHack and robot planning. |
ChronoBias: A Benchmark for Evaluating Temporal Group Bias in the Time-sensitive Knowledge of Large Language Models (2025.findings-emnlp)
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| Challenge: | Using a template-based semi-automated generation method, we evaluate time-conditional group bias in time-sensitive knowledge of large language models (LLMs). |
| Approach: | They propose a template-based semi-automated generation method to construct a time-conditional group bias benchmark. |
| Outcome: | The proposed method balancing quality-quantity trade-off in existing benchmark curation approaches. |
Reliability-Aware Adaptive Self-Consistency for Efficient Sampling in LLM Reasoning (2026.findings-acl)
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| Challenge: | Self-consistency improves reasoning reliability but incurs substantial inference cost . Adaptive self-consistent methods rely on count-based stopping rules that treat all responses equally . |
| Approach: | They propose a method that reframs adaptive sampling from response counting to evidence sufficiency by leveraging response-level confidence. |
| Outcome: | The proposed method reduces inference cost by up to 70% while preserving accuracy on GSM8K. |
On Sample-Efficient Code Generation (2023.emnlp-industry)
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Hojae Han, Yu Jin Kim, Byoungjip Kim, Youngwon Lee, Kyungjae Lee, Kyungmin Lee, Moontae Lee, Kyunghoon Bae, Seung-won Hwang
| Challenge: | Existing approaches to code generation rely on rejection sampling to generate multiple code snippets then select the best. |
| Approach: | They propose a framework that prioritizes sampling on test problems that models can solve. |
| Outcome: | The proposed framework reduces sampling costs while maintaining comparable code generation performance. |
Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning (2023.acl-long)
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Kyuyong Shin, Hanock Kwak, Wonjae Kim, Jisu Jeong, Seungjae Jung, Kyungmin Kim, Jung-Woo Ha, Sang-Woo Lee
| Challenge: | Recent studies have proposed unified user modeling frameworks that leverage user behavior data from various applications. |
| Approach: | They propose to use user behavior sequences as plain text to represent rich information in any domain or system without losing generality. |
| Outcome: | The proposed frameworks achieve excellent results on diverse recommendation tasks and can be used on unseen domains and services. |
Contrastive Learning as a Polarizer: Mitigating Gender Bias by Fair and Biased sentences (2024.findings-naacl)
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| Challenge: | Recent studies have highlighted social biases inherent in training data can lead models to learn and propagate them. |
| Approach: | They propose a contrastive learning method that uses anchor points to push further negatives and pull closer positives within the representation space. |
| Outcome: | The proposed method achieves state-of-the-art in the ICAT score on the StereoSet, a benchmark for measuring bias in models. |